
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			['''image_processor''', '''tokenizer''']
     lowerCAmelCase_							   =			'''CLIPImageProcessor'''
     lowerCAmelCase_							   =			('''CLIPTokenizer''', '''CLIPTokenizerFast''')



     def __init__(				self							:							Dict     ,						_A							:							List[str]=None     ,						_A							:							List[Any]=None     ,						**_A							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''     ,						_A     ,						)
         __SCREAMING_SNAKE_CASE      :	str					=		kwargs.pop('''feature_extractor''' )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.''' )
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.''' )

       super().__init__(_A     ,						_A )



     def __call__(				self							:							List[str]     ,						_A							:							Union[str, Any]=None     ,						_A							:							Union[str, Any]=None     ,						_A							:							Tuple=None     ,						**_A							:							Optional[int] ):







       """simple docstring"""




       if text is None and images is None:
         raise ValueError('''You have to specify either text or images. Both cannot be none.''' )

       if text is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.tokenizer(_A     ,						return_tensors=_A     ,						**_A )

       if images is not None:
         __SCREAMING_SNAKE_CASE      :	str					=		self.image_processor(_A     ,						return_tensors=_A     ,						**_A )

       if text is not None and images is not None:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		image_features.pixel_values
         return encoding
       elif text is not None:
         return encoding
       else:
         return BatchEncoding(data=dict(**_A )     ,						tensor_type=_A )



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						*_A							:							Tuple     ,						**_A							:							Any ):







       """simple docstring"""




       return self.tokenizer.batch_decode(*_A     ,						**_A )



     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						*_A							:							Optional[Any]     ,						**_A							:							Optional[Any] ):







       """simple docstring"""




       return self.tokenizer.decode(*_A     ,						**_A )



     @property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		self.tokenizer.model_input_names
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,						_A     ,						)
       return self.image_processor_class




     @property
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,						_A     ,						)
       return self.image_processor

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
